from bisect import bisect_right
from collections import Counter

import torch
from timm.scheduler.cosine_lr import CosineLRScheduler
from timm.scheduler.scheduler import Scheduler
from timm.scheduler.step_lr import StepLRScheduler


def build_scheduler(args, optimizer, n_iter_per_epoch):
    num_steps = int(args.epoch * n_iter_per_epoch)
    warmup_steps = int(args.warmpup_epoch * n_iter_per_epoch)
    decay_steps = int(args.decay_epoch * n_iter_per_epoch)
    multi_steps = [i * n_iter_per_epoch for i in []]

    lr_scheduler = None
    if args.lr_scheduler_name == "cosine":
        lr_scheduler = CosineLRScheduler(
            optimizer,
            t_initial=num_steps,
            t_mul=1.0,
            lr_min=args.min_lr,
            warmup_lr_init=args.warmup_lr,
            warmup_t=warmup_steps,
            cycle_limit=1,
            t_in_epochs=False,
        )
    elif args.lr_scheduler_name == "linear":
        lr_scheduler = LinearLRScheduler(
            optimizer,
            t_initial=num_steps,
            lr_min_rate=0.01,
            warmup_lr_init=args.warmup_lr,
            warmup_t=warmup_steps,
            t_in_epochs=False,
        )
    elif args.lr_scheduler_name == "step":
        lr_scheduler = StepLRScheduler(
            optimizer,
            decay_t=decay_steps,
            decay_rate=args.lr_decay_rate,
            warmup_lr_init=args.warmup_lr,
            warmup_t=warmup_steps,
            t_in_epochs=False,
        )
    elif args.lr_scheduler_name == "multistep":
        lr_scheduler = MultiStepLRScheduler(
            optimizer,
            milestones=multi_steps,
            gamma=args.lr_gamma,
            warmup_lr_init=args.warmup_lr,
            warmup_t=warmup_steps,
            t_in_epochs=False,
        )

    return lr_scheduler


class LinearLRScheduler(Scheduler):
    def __init__(
        self,
        optimizer: torch.optim.Optimizer,
        t_initial: int,
        lr_min_rate: float,
        warmup_t=0,
        warmup_lr_init=0.0,
        t_in_epochs=True,
        noise_range_t=None,
        noise_pct=0.67,
        noise_std=1.0,
        noise_seed=42,
        initialize=True,
    ) -> None:
        super().__init__(
            optimizer,
            param_group_field="lr",
            noise_range_t=noise_range_t,
            noise_pct=noise_pct,
            noise_std=noise_std,
            noise_seed=noise_seed,
            initialize=initialize,
        )

        self.t_initial = t_initial
        self.lr_min_rate = lr_min_rate
        self.warmup_t = warmup_t
        self.warmup_lr_init = warmup_lr_init
        self.t_in_epochs = t_in_epochs
        if self.warmup_t:
            self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values]
            super().update_groups(self.warmup_lr_init)
        else:
            self.warmup_steps = [1 for _ in self.base_values]

    def _get_lr(self, t):
        if t < self.warmup_t:
            lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
        else:
            t = t - self.warmup_t
            total_t = self.t_initial - self.warmup_t
            lrs = [v - ((v - v * self.lr_min_rate) * (t / total_t)) for v in self.base_values]
        return lrs

    def get_epoch_values(self, epoch: int):
        if self.t_in_epochs:
            return self._get_lr(epoch)
        else:
            return None

    def get_update_values(self, num_updates: int):
        if not self.t_in_epochs:
            return self._get_lr(num_updates)
        else:
            return None


class MultiStepLRScheduler(Scheduler):
    def __init__(
        self, optimizer: torch.optim.Optimizer, milestones, gamma=0.1, warmup_t=0, warmup_lr_init=0, t_in_epochs=True
    ) -> None:
        super().__init__(optimizer, param_group_field="lr")

        self.milestones = milestones
        self.gamma = gamma
        self.warmup_t = warmup_t
        self.warmup_lr_init = warmup_lr_init
        self.t_in_epochs = t_in_epochs
        if self.warmup_t:
            self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values]
            super().update_groups(self.warmup_lr_init)
        else:
            self.warmup_steps = [1 for _ in self.base_values]

        assert self.warmup_t <= min(self.milestones)

    def _get_lr(self, t):
        if t < self.warmup_t:
            lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
        else:
            lrs = [v * (self.gamma ** bisect_right(self.milestones, t)) for v in self.base_values]
        return lrs

    def get_epoch_values(self, epoch: int):
        if self.t_in_epochs:
            return self._get_lr(epoch)
        else:
            return None

    def get_update_values(self, num_updates: int):
        if not self.t_in_epochs:
            return self._get_lr(num_updates)
        else:
            return None
